{"id":"W4390038409","doi":"10.1021/acs.iecr.3c02849","title":"A Generalizable Method for Capacity Estimation and RUL Prediction in Lithium-Ion Batteries","year":2023,"lang":"en","type":"article","venue":"Industrial & Engineering Chemistry Research","topic":"Advanced Battery Technologies Research","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Overfitting; Robustness (evolution); Kriging; Smoothing; Generalizability theory; Bootstrapping (finance); Artificial neural network; Gaussian process; Battery (electricity); Machine learning; Artificial intelligence; Gaussian; Statistics; Mathematics; Econometrics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001404307,0.0001814819,0.0002209773,0.0003799661,0.00009191256,0.00009199595,0.0002359397,0.0004196786,0.0000230411],"category_scores_gemma":[0.001437571,0.0002076764,0.00003127553,0.00124118,0.000073457,0.0002235309,0.0001499813,0.001002502,0.00000993584],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003524346,"about_ca_system_score_gemma":0.00003750639,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003532707,"about_ca_topic_score_gemma":0.000001729912,"domain_scores_codex":[0.9982139,0.0000331925,0.0002887952,0.0003367517,0.0003870391,0.0007403233],"domain_scores_gemma":[0.9990104,0.0005143307,0.00001552895,0.0002732227,0.00008780386,0.00009866882],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001849481,0.000006637595,0.0001910341,0.0002982782,0.00001429874,0.000005578288,0.00006073141,0.3767286,0.6131718,0.0000224569,0.002608596,0.006873521],"study_design_scores_gemma":[0.0004127354,0.00002234711,0.00009982777,0.00007355907,0.000001708158,0.000005988144,0.00005055088,0.5565394,0.4392744,0.0002299149,0.003179912,0.0001096743],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9697564,0.00009016765,0.02698499,0.0003658596,0.0002538311,0.000749793,0.0001262036,0.00146298,0.0002097627],"genre_scores_gemma":[0.982878,0.0002117048,0.01293849,0.000003876338,0.0007902228,0.001639996,0.000245376,0.000170264,0.001122061],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1798108,"threshold_uncertainty_score":0.8468798,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.119010702520606,"score_gpt":0.3594697910797477,"score_spread":0.2404590885591417,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}